NEURAL NETWORKS — The Backbone of Deep-Learning : Use-Cases and their Applications in Industry
→ ⚡Our intelligence is what makes us human, and AI is an extension of that quality⚡ ←
Today’s Artificial Intelligence is about new ways of connecting people to computers, people to knowledge, people to the physical world, and people to people.
In this article, we’ll discuss about Neural Networks , it’s applications and it’s industry based use-cases.🙌 Before going through Neural Network, it is important to know that Deep learning is a subfield of machine learning, and neural networks make up the backbone of deep learning algorithms. Neural Networks can be taught to perform complex tasks and do not require programming as conventional computers.
Let’s discuss more about this…..🌠
📍What are Neural Networks?
Neural networks are a set of algorithms, they are designed to mimic the human brain, that is designed to recognize patterns. They interpret data through a form of machine perception by labeling or clustering raw input data.
📍How do Artificial Neural Networks Work?
In neural networks, when data is collected about a particular process, the model that is used to learn about and understand that process and predict how that process will perform in the future is a simplified representation of how a brain neuron works.
A brain neuron receives an input and based on that input, fires off an output that is used by another neuron. The neural network simulates this behavior in learning about collected data and then predicting outcomes.
⚡Let’s take a moment to consider the human brain. Made up of a network of neurons, the brain is a very complex structure.
It’s capable of quickly assessing and understanding the context of numerous different situations. Computers struggle to react to situations in a similar way. Artificial Neural Networks are a way of overcoming this limitation. They attempt to simulate the way the brain operates. Sometimes called perceptrons , an Artificial Neural Network is a hardware or software system.
Some networks are a combination of the two. Consisting of a network of layers, this system is patterned to replicate the way the neurons in the brain operate. The network comprises an input layer, where data is entered, and an output layer. The output layer is where processed information is presented. Connecting the two is a hidden layer or layers. The hidden layers consist of units that transform input data into useful information for the output layer to present.
In addition to replicating the human decision making progress Artificial Neural Networks allow computers to learn. Their structure also allows ANN’s to reliably and quickly identify patterns that are too complex for humans to identify. Artificial Neural Networks also allow us to classify and cluster large amounts of data quickly.
📍How Companies Are Using Neural Networks?
Companies are using neural networks in various ways, depending on their business model. LinkedIn for instance, uses neural networks along with linear text classifiers to detect spam or abusive content in its feeds when it is created. It also used neural networks to help understand all kinds of content shared on LinkedIn — ranging from news articles to jobs to online classes — so that ,it can build better recommendation and search products for members and customers⚡.
📌MASTERCARD — Fraud Detection Applications
As technology advances, and more importance is placed on online transactions, fraudsters are also becoming more sophisticated. Luckily Artificial Neural Networks can help to keep us, and our finances, safe.
Deep learning and Artificial Neural Networks applications are powering systems capable of detecting all forms of financial fraud.
For example, this application can identify unusual activity, such as transactions occurring outside the established time frame. Visa has used smart solutions to cut credit card fraud by two thirds. Their sophisticated anti-fraud detection systems are working towards biometric solutions. However the company also analysis information such as payment method, time, location, item purchased, and the amount spent.
Even a small deviation from the norm in any of these categories can highlight a potential fraud case. Within seconds smart solutions allow Visa to look at over 500 data elements to determine if a transaction is suspicious. Similarly, it can be embarrassing when our card is declined by a retailer. Especially if our account is in credit.
MasterCard is employing solutions powered by Artificial Neural Networks to reduce the chances of this happening🔥. Currently, MasterCard has halved the chances of these errors from occurring.
📌FACEBOOK — Facial Recognition Software Applications
Technology companies have long been working toward developing reliable facial recognition software.
One company leading the way is Facebook. For a number of years now they have been using the facial recognition technology to auto-tag uploaded photographs. They have also developed “DeepFace”🔥.
DeepFace is a form of facial recognition software-driven by Artificial Neural Networks⚡. It is capable of mapping 3D facial features. Once the mapping is complete the software turns the information into a flat model. The information is then filtered, highlighting distinctive facial elements. To be able to do this DeepFace implements 120 million parameters. This technology hasn’t just emerged overnight. DeepFace has been trained with a pool of 4.4 million tagged faces. These images were taken from 4,000 different Facebook accounts.
During the training process, tests were carried out presenting the system with side-by-side images. The system was then asked to identify if the images are of the same person. In these tests, DeepFace returned an accuracy rating of 97.25%. Human participants taking the same test scored, on average, 97.5%. Facebook has also taken its software to computing and technology conferences.
This is done with the purpose of allowing academics and researchers to assess and inspect the technology. With all this work, it’s little wonder that DeepFace may be the most accurate facial technology software yet developed⚡.
📍SOME OTHER APPLICATIONS OF NEURAL NETWORKS
Artificial neural networks have become an accepted information analysis technology in a variety of disciplines. This has resulted in a variety of commercial applications (in both products and services) of neural network technology. The applications that neural networks have been put to and the potential possibilities that exist in a variety of civil and military sectors are tremendous.
Given below are domains of commercial applications of neural network technology:-
- Business — Marketing & Real Estate
- Document & Form Processing — Machine printed character recognition , Graphics recognition ,Hand printed character recognition & Cursive handwritten character recognition
- Finance Industry — Market trading ,Fraud detection & Credit rating
- Food lndustry — Odour/aroma analysis, Product development & Quality assurance
- Energy Industry — Electrical load forecasting ,Hydroelectric dam operation & Natural gas
- Manufacturing — Process control & Quality control
- Medical & Health Care Industry — Image analysis, Drug development & Resource allocation
- Science & Engineering — Chemical engineering ,Electrical engineering & Weather forecasting
Artificial Neural Networks are Revolutionising Business Practises ,from streamlining manufacturing to product suggestions and facial scanning, Artificial Neural Networks are transforming the way businesses operate.
We can also say that artificial intelligence, artificial neural networks, and deep learning will eventually play a far more active role in retraining our brains, particularly as brain-computer interfaces become more prevalent and widely used. Deep learning and Neural networks will be essential for learning to read and interpret an individual brain’s language, and it will be used to optimize a different aspect of thought — focus, analysis, introspection.
Therefore, the use of neural networks seems unstoppable⚡. With the advancement of computer and communication technologies, the whole process of doing business has undergone a massive change. More and more knowledge-based systems have made their way into a large number of companies.
Hence, Neural networks are sets of algorithms intended to recognize patterns and interpret data through clustering or labeling⚡.
⭐FUTURE OF NEURAL NETWORKS — “Computers are able to see, hear and learn. Welcome to the Future.”⭐